Applying machine learning to agricultural data

Abstract

Many techniques have been developed for abstracting, or "learning," rules and relationships from diverse data sets, in the hope that machines can help in the often tedious and error-prone process of acquiring knowledge from empirical data. While these techniques are plausible, theoretically well-founded, and perform well on more or less artificial test data sets, they stand or fall on their ability to make sense of real-world data. This paper describes a project that is applying a range of learning strategies to problems in primary industry, in particular agriculture and horticulture. We briefly survey some of the more readily applicable techniques that are emerging from the machine learning research community, describe a software workbench that allows users to experiment with a variety of techniques on real-world data sets, and detail the problems encountered and solutions developed in a case study of dairy herd management in which culling rules were inferred from a medium-sized database of herd information.